Spectral Libraries: Productivity Enhancers for Cancer Proteomics

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Spectral LibrariesProductivity Enhancers for
Cancer Proteomics
Christopher R. Kinsinger Ph.D.National Cancer
InstituteClinical Proteomic Technologies for
Cancer
NCI Board of Scientific Advisors June 22, 2009
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Todays presentation

• Outline
• State of cancer proteomics
• Critical role of Spectral Libraries
• In discovery proteomics
• In targeted proteomics
• Proposed concept
• Questions

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Current investment in proteomics
NCI/NIH funding in proteomics
Number of new FDA-approved protein analytes
Dollars ( millions)
What if a small project could increase the
efficiency of these dollars by 50?
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Enhancing discovery- and verification (targeted)-
stage components in a biomarker development
pipeline
Biomarkers worth evaluating
Found in blood? Higher in cancer?
Biomarker candidates

• untargeted proteomics
• genomics

hypotheses
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Current state of peptide identification
Proteomics experiment
Protein mixture Physical digest Mass spec analysis
?
Match product ion spectra Score
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The Spectral Library solution
What is a Spectral Library?

• Used in chemical, drug, forensics industry
• Catalog of the highest quality MS spectra
• Proven method to identify unknown spectra
• Maintained by NIST

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The Spectral Library Solution
What is a Spectral Library?
How is a Spectral Library built?
Clean data IntoSpectral Library
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The Spectral Library Solution

• Adds a 2nd dimension of search
• Intensity of peaks
• Peptide sequence
• Improves speed and reliability in peptide ID
• Compilation of all observable peptides

What is a Spectral Library?
How is a Spectral Library built?
What are the advantages?
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How spectral libraries work
Theoretical spectrum
Physical spectrum
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Enhancing discovery-stagerecombinant proteins

• Enhancing identification of low abundant proteins

• 48 human proteins spiked into yeast
• High-end MS platforms
• 500 increase in identification!

Proteins identified
Spectral library enables identification of
low-abundant proteins
Concentration (fmol/uL)
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Enhancing discovery-stageclinical tissue
Colon tissue data
Enriching library enriches discovery
Proteins identified
2008 library Vanderbilt tissue data
2008 library
non-library methods
Discovery stage
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Spectral libraries for verification (targeted)
stage proteomics
(Multiple Reaction Monitoring)

• Quantitative mass spectrometry

From Steve Carr
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Enhancing verification-stage

• Steps to developing a quantitative protein assay
• Select 3-5 target peptides
• Representative of parent protein
• Detectable by mass spectrometer
• Synthesize labeled peptides
• Develop anti-peptide antibodies
• Analyze on robust, affordable instrument platform

Provided by Spectral Library
Anderson, et al. Mol. Cell. Prot. 2009 in press
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Overview of proposed concept Adding value to
biomarker development
Goal Develop public library that anchors
proteomic analysis to the physical properties of
a peptide through its MS/MS spectrum

• Advantages of Spectral Library
• Accelerates and improves ID of low abundant
  proteins for discovery
• Provides increasing registry of known peptides
• Becomes an index of assay design
• Creates a community-wide resource and shared
  interest to foster interactions among diverse
  research groups

• Human Spectral Library
• High quality human biological samples
• Tissue
• Recombinant proteins
• High quality peptide spectra
• Coordination among data generators, library
  developers and data integrators

Strengthens the first stages of the biomarker
development pipeline
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Representation of cancer tissue in library is
dismal

• All other tissue types are lt1 of current library
• Project will catalog proteins from 15 tissue
  types
• Increase total number of peptides in library by
  50

Sample source of files in current library are from
Colon 2.31
CSF 1.07
Kidney 1.59
Liver 2.40
Lymph 6.15
Plasma 77.77
Red Blood Cells 2.95
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Further expanding a library with non-native
proteins

• Begin with tissue samples
• Identify key proteins missing from library (TCGA,
  SPOREs, ICBP, etc.)
• Fill gaps with recombinant proteins or synthetic
  peptides
• At least 70 of peptides are unmodified
• Complete coverage of protein
• Aids identification of high-priority,
  low-abundant proteins

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Evaluation criteria

• Increase number of peptide spectra in spectral
  library
• Increase number of proteins represented in
  library
• Provide a sustainable, caBIG-compatible data
  repository for proteomics data

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Components of conceptLeveraging NCI resources
and partners

• NCI resources
• Sample source (tissue, protein/peptide
  production)
• 2. Protein analysis (data generators)
• Partners NCI-F
• Generate high quality spectra to more extensively
  represent all human protein sequences
• 3. Data coordinating center
• Partner CBIIT/caBIG
• Maintain CPAS database of experiments, peptides,
  proteins, and raw spectra ensure quality of and
  completeness of annotation leverage caBIG data
  portal capabilities and Cancer Center network

• Leveraged activities
• 4. Spectral Library development
• Partner NIST
• Receive peptide spectra from CBIIT and
  incorporate into human spectral library
• 5. Data integration with other resources
• Partner NCBI
• Acquire data submitted to the NCI for
  incorporation into NIH Peptidome database

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Proposed Spectral Library(timeline budget)
FY12
FY11
FY10
Initiative title

1) Biospecimen tissue acquisition (OBBR)
1.1 million
666,667
433,333
2) Recombinant protein production (RFP)
300,000
600,000
300,000
3) Protein analysis (Data generators) (RFP)
800,000
800,000
800,000
2.4 million
4) Data coordinating center (CBIIT)
700,000
233,333
233,333
233,333
5) Spectral Library development (NIST)
Leveraged activity
6) Data integration with other NIH resources
(NCBI Peptidome)
Leveraged activity
1.7 million 1.7 million 1.3 million
4.8 million
Total
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Summary

• Registry of high quality, assigned peptide
  spectra
• Enhance biomarker development in both the
  discovery and verification phases
• Augment existing spectral libraries by 50 with
  spectra from cancer-relevant proteins
• Spectral libraries will increase efficiency of
  NCIs investment in proteomics